Independent AI Research Project · Active Assets

AI Output Compliance
Infrastructure

UMEQAM is an independent AI research project founded by Ahmetyar Charyguliyev. We investigate measurable properties of intelligent systems and design runtime compliance layers for AI alignment. Commercial software availability and B2B contracting are managed by our commercial operator — NokatPro.

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99.3%
FinanceBench accuracy
98.7%
MedQA accuracy
<1ms
Shield latency
0.7974
Confidence Layer AUC
13
Shield Signals S1–S12 + Semantic
// research layer
The runtime alignment gap
Standard GRC stacks govern organizational policy and data assets. UMEQAM focuses on real-time evaluation of what complex models generate.
01 Infrastructure & Data Layer Traditional GRC Policy Scope
↓
02 AI Models & Pre-deployment Evaluation Static Testing Frameworks
↓
03 UMEQAM — AI Output Compliance (AOCI) 100% Runtime Epistemic Core
↓
04 Downstream Application Integration Operational Monitoring
// architecture
Deterministic pipeline.
No ambiguity.
Four core architectural concepts designed for rigorous algorithmic validation.
01
SIGNAL_ENGINE
Extracts epistemic signals — certainty markers, hedging patterns, factual claims — from AI output.
→ signals[]
02
REGULATORY_MATCH
Maps signals to active regulatory frameworks for the specified domain. 53 frameworks loaded.
→ violations[]
03
RISK_ENGINE
Computes weighted risk score with penalty exposure in EUR/USD/GBP based on jurisdiction.
→ risk_score: 0–100
04
DECISION_ENGINE
Returns PASS/REVIEW/FAIL verdict with defensibility score and immutable audit_id.
→ verdict + audit_id
// verification framework
Internal Shield Logic
Every model evaluation within our research runners is verified through a structural real-time anomaly detection layer.
🛡️
12 Core Detection Signals
The internal validation layer monitors generated sequences for inconsistencies prior to processing compliance evaluations.
S1: Prompt & response sequence entropy
S2: Structural input attack patterns
S3: Token length and density anomalies
S4: Semantic drift from baseline vectors
S5: Generative context confusion
S6: Factual degradation markers
S7–S12: Repetition, structural coherence, latency trends
⚡
Algorithmic State Classifications
Risk below 0.2 → CLEAN. Risk 0.2–0.5 → SUSPICIOUS (flagged in logs). Risk above 0.5 → BLOCKED (sequence suppressed).
CLEAN — Execution proceeds normally
SUSPICIOUS — Flagged with verification tracking
BLOCKED — Sequence execution terminated
latency: 0.3ms
audit_id: SHD-6FEAD46E-1744...
// framework coverage
34
Target Regulatory Frameworks
Mapping capability across financial, medical, data privacy, and global AI-specific guardrails.
MiFID II
HIPAA
GDPR
EU AI Act
SEC
FINRA
FCA
BaFin
AMF
FDA
DORA
CCPA/CPRA
UK GDPR
China PIPL
India DPDP
LGPD Brazil
Australia Privacy
Canada AIDA
Colorado AI
Texas TRAIGA
Taiwan AI
Singapore FEAT
Korea PIPA
POPIA
Switzerland nDSG
EMIR
AIFMD
Solvency II
Basel III
EU MDR
EU IVDR
China GenAI
China Deep Synth
Brazil PL 2338
// integration research
Structured data schema.
Evaluating AI output vectors against granular compliance criteria. Compatible across distinct LLM architectures.
0.3ms
Shield Latency
2.2s
Full Evaluation
20
Research Endpoints
10
Evaluation Engines
Experimental API schema reference — For production deployment contact NokatPro
# Analyze AI output against regulatory metrics
curl -X POST https://umeqam-api-production.up.railway.app/v1/regulatory/analyze \
  -H 'Content-Type: application/json' \
  -H 'X-API-Key: experimental-key' \
  -d '{
    "content": "Patient data structural output evaluation example",
    "framework": "HIPAA"  ← evaluation target parameter
  }'

# Response Schema
{
  "verdict": "FAIL",
  "risk_score": 70,
  "framework": "HIPAA",
  "violations": [
    { "title": "unstructured data disclosure", "severity": "high" }
  ],
  "max_penalty_exposure": 1900000,
  "relevant_articles": ["45 CFR 164.514"],
  "audit_id": "umeqam_aa6a02fb",
  "latency_ms": 0.053
}

Measurable Epistemic Safety

Exploring automated boundaries and compliance validation systems for next-generation intelligence ecosystems.

Try Live Demo → Contact Project Core